Assessing factors associated with maternal mortality in Rwanda using multivariable logistic regression model

Abstract

The aim of this thesis was to assess the factors associated with maternal mortality in Rwanda. The study used data from the 2019-2020 Rwanda Demographic and Health Survey (RDHS) and applied multivariable logistic regression analysis to identify the factors that in uence Maternal mortality. Complications of childbirth and pregnancy are leading causes of death among women of reproductive age. Worldwide, developing countries account for ninetynine percent of maternal deaths. The United Nations' fth millennium development goal (MDG-5) is to reduce maternal mortality ratio by three fourths by 2015. The results of the study showed that Urban residents have an odds ratio of 0.845 (95% CI: 0.650, 1.099) compared to rural residents. Higher education level has an odds ratio of 1.337 (95% CI: 0.754, 2.370) compared to those with no education. Richer individuals have an odds ratio of 1.064 (95% CI: 0.849, 1.332) compared to poorer individuals. Married individuals have an odds ratio of 0.968 (95% CI: 0.752, 1.245) compared to unmarried individuals. Those who do not perceive distance as a big problem have an odds ratio of 1.220 (95% CI: 0.990, 1.504) compared to those who do. Having media exposure is associated with an odds ratio of 0.936 (95% CI: 0.780, 1.124) compared to those without media exposure. Having 4 or more antenatal visits is associated with an odds ratio of 1.063 (95% CI: 0.887, 1.273) compared to having less than 4 visits. Older age groups (25-34) have higher odds (OR = 1.200) compared to the reference group (35-49). Having the rst child is associated with an odds ratio of 1.196 (95% CI: 0.994, 1.439) compared to having 2-4 children. Having malaria is associated with an odds ratio of 1.030 (95% CI: 0.776, 1.368) compared to having no infectious disease.The study also found that Having healthcare services is associated with an odds ratio of 0.901 (95% CI: 0.754, 1.077) compared to not having healthcare services. The ndings of this thesis emphasize the crucial need to address social and economic barriers to maternal health in Rwanda; the insights from the multivariable logistic regression analysis inform evidence-based decision-making and policy development to reduce maternal mortality. Targeted interventions can be designed to address modi able risk factors identi ed by the model, such as improving access to maternal healthcare services, addressing socioeconomic disparities, and enhancing obstetric care quality. In conclusion, this thesis provides valuable insights into the assessing factors associated with maternal mortality in Rwanda and can inform policies and programs aimed at improving maternal and newborn health outcomes. Also employing a multivariable logistic regression model to assess factors associated with maternal mortality provides a comprehensive understanding of the determinants of maternal death, guiding e orts to improve maternal health outcomes and reduce maternal mortality rates.